This report contains different plots and tables that may be relevant for analysing the results. Observe:

Statistics for alg1

Given a problem consisting of \(m\) subproblems with \(Y_N^s\) given for each subproblem \(s\), we use a filtering algorithm to find \(Y_N\) (alg1).

The following instance/problem groups are generated given:

Status

  • 690/1600 problems have been solved, i.e. 910 remaining.
  • 680/1600 problems have 5 instances solved for each configuration.
  • 1/1600 have not been classified.
  • 81/1600 have not been fully classified (only classified extreme).

Problems solved for the analysis

Note that the width of objective \(i = 1, \ldots p\), \(w_i = [l_i, u_i]\) should be approx. \(10000m\). Check:

## # A tibble: 4 × 6
##       m mean_width1 mean_width2 mean_width3 mean_width4 mean_width5
##   <dbl>       <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
## 1     2      19317.      19288.      19326.      19135.      18887.
## 2     3      28356.      28479.      28149.      28047.      27084.
## 3     4      38363.      38506.      38373.        NaN         NaN 
## 4     5      47502.      47820.      47251.        NaN         NaN

Size of \(Y_N\)

What is \(|Y_N|\) given the different methods of generating the set of nondominated points for the subproblems?

## # A tibble: 4 × 3
##   method mean_card     n
##   <chr>      <dbl> <int>
## 1 l         61102.   170
## 2 m         59347.   170
## 3 u         59424.   170
## 4 ul        56146.   170

Does \(p\) have an effect?

## # A tibble: 16 × 4
## # Groups:   method [4]
##    method     p mean_card     n
##    <chr>  <dbl>     <dbl> <int>
##  1 l          2     3248.    50
##  2 m          2     2295.    50
##  3 u          2      741.    50
##  4 ul         2      946.    50
##  5 l          3    18814.    50
##  6 m          3    14861.    50
##  7 u          3     4405.    50
##  8 ul         3     6358.    50
##  9 l          4    91921.    35
## 10 m          4   107441.    35
## 11 u          4    95680.    35
## 12 ul         4    90554.    35
## 13 l          5   173344.    35
## 14 m          5   156307.    35
## 15 u          5   185597.    35
## 16 ul         5   171720.    35

Does \(m\) have an effect?

## # A tibble: 16 × 4
## # Groups:   method [4]
##    method     m mean_card     n
##    <chr>  <dbl>     <dbl> <int>
##  1 l          2    37198.   100
##  2 m          2    43615.   100
##  3 u          2    36622.   100
##  4 ul         2    35269.   100
##  5 l          3   148973.    40
##  6 m          3   131004.    40
##  7 u          3   157511.    40
##  8 ul         3   145011.    40
##  9 l          4    21163     20
## 10 m          4    15508.    20
## 11 u          4     4136.    20
## 12 ul         4     6950.    20
## 13 l          5    28539.    10
## 14 m          5    17719.    10
## 15 u          5     5657.    10
## 16 ul         5     7844.    10

Relative size of \(Y_N\)

Nondominated points classification

We classify the nondominated points into, extreme, supported non-extreme and unsupported.

Plots used in the paper